What it means for makers and artists
A few days ago, I ran an experiment where I gave a 1.2B parameter language model six different personas and played poker with them against each other. The results were fascinating: one player never lost but never won, while another dominated nearly half the time.
The key insight here is that the difference between these personalities isn’t just in how they’re described; it’s in how they actually play. This experiment demonstrates that personality prompts in language models aren’t mere flavor text—they significantly alter an AI’s behavior and outcomes.
Key Takeaways
- The same model, the same cards, but different personas led to drastically different performance metrics.
- A single paragraph of character description could shift a player from never losing to winning nearly half the time.
- This experiment underscores the importance of carefully crafting AI personalities and their impact on gameplay outcomes.
Details
The results:
| Personality | Wins | Eliminated | Avg Place |
|---|---|---|---|
| Shark (patient, calculating) | 45 | 32% | 2.3 |
| Maniac (fearless, relentless) | 24 | 50% | 3.0 |
| Gambler (optimistic, stubborn) | 21 | 51% | 3.6 |
| Tilter (emotional, revenge-driven) | 10 | 80% | 5.1 |
| Grinder (cautious, methodical) | 0 | 0% | 2.7 |
| Rock (disciplined, conservative) | 0 | 63% | 4.3 |
The character that fascinated me most was the Grinder (like fr).
Zero wins. In 100 tournaments. But also zero eliminations; it survived every single game. Every time, it finished 2nd or 3rd. Never first, never last.
The Tilter was the opposite story.
Told to “never let a bad beat go unanswered,” the Tilter won 10 tournaments but was eliminated in 80 of them. When it won, it won big. When it lost, it spiraled: lose a hand, escalate the next one, lose bigger, go broke. The revenge-driven personality creates a death spiral. Boom or bust, nothing in between.
The Shark just quietly dominated.
45 wins out of 100 nearly half. Same model as every other player at the table. The only difference was a paragraph that said “patient, calculating, predatory.” It picked its spots, punished the weaker players, and avoided unnecessary risk. The model actually interpreted the nuance between “be aggressive” (Maniac: 24 wins) and “be selectively aggressive” (Shark: 45 wins).
What surprised me:
- A paragraph of personality text, maybe 50 words, created a 45-to-0 win differential between the best and worst personalities.
- The model is the same. The cards are random. The only variable is who the AI thinks it is.
- This experiment demonstrates that personality prompts in language models aren’t mere flavor text—they significantly alter an AI’s behavior and outcomes.
If you want to try it yourself:
- Hive: the agent framework (
pip install hive-agent) - Hive Arena: the experiment runner with persona profiles
- PokerTable: the poker engine (
pip install pokertable)
The persona profiles are YAML files in the repo. You just need a local model running via LM Studio or Ollama.
References
Everything is open source and runs locally.
The persona profiles are YAML files in the repo. You just need a local model running via LM Studio or Ollama.
Originally published at reddit.com. Curated by AI Maestro.
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